GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
A critique of the sensitivity rules usually employed for statistical table protection
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Disclosure risk assessment in statistical data protection
Journal of Computational and Applied Mathematics - Special Issue: Proceedings of the 10th international congress on computational and applied mathematics (ICCAM-2002)
Privacy in Statistical Databases: CASC Project International Workshop, PSD 2004, Barcelona, Spain, June 9-11, 2004, Proceedings (Lecture Notes in Computer Science)
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
Using a Mathematical Programming Modeling Language for Optimal CTA
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
A Shortest-Paths Heuristic for Statistical Data Protection in Positive Tables
INFORMS Journal on Computing
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Proceedings of the 2010 international conference on Privacy in statistical databases
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Eliminating small cells from census counts tables: some considerations on transition probabilities
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Automatic structure detection in constraints of tabular data
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Enhanced controlled tabular adjustment
Computers and Operations Research
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One of the main concerns of national statistical agencies (NSAs) is to publish tabular data. NSAs have to guarantee that no private information from specific respondents can be disclosed from the released tables. The purpose of the statistical disclosure control field is to avoid such a leak of private information. Most protection techniques for tabular data rely on the formulation of a large mathematical programming problem, whose solution is computationally expensive even for tables of moderate size. One of the emerging techniques in this field is controlled tabular adjustment (CTA). Although CTA is more efficient than other protection methods, the resulting mixed integer linear problems (MILP) are still challenging. In this work a heuristic approach based on block coordinate descent decomposition is designed and applied to large hierarchical and general CTA instances. This approach is compared with CPLEX, a state-of-the-art MILP solver. Our results, from both synthetic and real tables with up to 1,200,000 cells, 100,000 of them being sensitive (resulting in MILP instances of up to 2,400,000 continuous variables, 100,000 binary variables, and 475,000 constraints) show that the heuristic block coordinate descent has a better practical behavior than a state-of-the-art solver: for large hierarchical instances it provides significantly better solutions within a specified realistic time limit, as required by NSAs in real-world.